CN116402492B - Household appliance maintenance early warning method and related equipment thereof - Google Patents
Household appliance maintenance early warning method and related equipment thereof Download PDFInfo
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Abstract
The application belongs to the technical field of electrical appliance maintenance, and discloses a home appliance maintenance early warning method and related equipment thereof, wherein the method comprises the following steps: when a fault report sent by a fault household appliance is received, acquiring operation parameters of the fault household appliance in a plurality of latest preset time periods before the fault report is sent, marking the operation parameters as comparison parameters, and acquiring operation parameters of related household appliances in a neighboring area of the fault household appliance in a latest preset time period, marking the operation parameters as target parameters; calculating the effective similarity between the target parameters and the comparison parameters of each related household appliance; judging whether each related household appliance has fault risks according to the effective similarity; sending maintenance early warning information to an associated user side of the related household electrical appliance with fault risk; therefore, similar faults of related household appliances can be effectively prevented, and the method is simple and practical.
Description
Technical Field
The application relates to the technical field of electrical appliance maintenance, in particular to a household appliance maintenance early warning method and related equipment thereof.
Background
In general, when a home appliance is in a fault, a user reports the fault, and the user cannot normally use the home appliance in a period from the fault to the completion of maintenance, which causes inconvenience. Therefore, some home appliance maintenance early warning methods appear in the prior art, the time of future failure is predicted before the home appliance fails, and then early warning is carried out according to the predicted time, so as to inform a user or manufacturer to timely overhaul the home appliance.
At present, the method for performing maintenance and early warning on the household electrical appliance generally collects working parameters (such as voltage, current and the like) of the household electrical appliance during working, then inputs the working parameters into a neural network model for analysis so as to predict future fault time and perform early warning in time, however, in order to obtain a reliable neural network model, a large amount of effective data is required to be used for training, but in practical application, enough data is often difficult to obtain for training, so that popularization and application cannot be realized. Therefore, a simple and practical home appliance maintenance early warning method is required.
Disclosure of Invention
The application aims to provide a simple and practical household appliance maintenance early warning method and related equipment thereof.
In a first aspect, the application provides a home appliance maintenance early warning method, which is applied to a server, wherein the server is in communication connection with a plurality of home appliances; the household appliance maintenance early warning method comprises the following steps:
A1. When a fault report sent by a fault household appliance is received, acquiring operation parameters of the fault household appliance in a plurality of latest preset time periods before the fault report is sent, marking the operation parameters as comparison parameters, and acquiring operation parameters of related household appliances in a neighboring area of the fault household appliance in the latest preset time periods, marking the operation parameters as target parameters;
A2. calculating the effective similarity between the target parameter and each comparison parameter of each related household appliance;
A3. Judging whether each related household appliance has a fault risk according to the effective similarity;
A4. And sending maintenance early warning information to the associated user side of the related household electrical appliance with fault risk.
When the household electrical appliance is in fault, the method takes the operation parameter of the fault household electrical appliance at a certain time before the fault as a reference, and is used for comparing the latest actual operation parameter of the related household electrical appliance in the area adjacent to the fault household electrical appliance, so as to determine whether each related household electrical appliance has fault risk, and further, when the fault risk exists, the method timely sends maintenance early warning information so as to remind the corresponding household electrical appliance to maintain, thereby preventing the related household electrical appliance from having similar faults; because the natural environment conditions born by all the household appliances in the adjacent area are similar, similar faults are easier to occur, when the household appliances are in fault, the probability of similar faults of the related household appliances in the adjacent area is high, the related household appliances in the adjacent area are detected in the mode, the related household appliances can be effectively prevented from being in subsequent similar faults, and compared with the prior art, the training of a neural network model is not required to be performed by collecting a large amount of effective data, and the method is simpler and more practical.
Optionally, the operation parameter includes one of an operation current, an operation voltage, operation vibration data, an operation noise, an operation temperature, and an operation pressure;
The step A2 comprises the following steps:
And calculating the similarity between one operation parameter included in the target parameters of each related household appliance and one operation parameter included in each comparison parameter, and taking the similarity as the effective similarity between the target parameters of each related household appliance and each comparison parameter.
Optionally, the operating parameters include a plurality of operating current, operating voltage, operating vibration data, operating noise, operating temperature, operating pressure;
The step A2 comprises the following steps:
Determining one of the operating parameters as an effective operating parameter according to the comparison parameter;
and calculating the similarity of the effective operation parameters in the target parameters of each related household appliance and the effective operation parameters in the comparison parameters, and taking the similarity as the effective similarity of the target parameters of each related household appliance and the comparison parameters.
Optionally, the step of determining one of the operating parameters as an effective operating parameter according to the comparison parameter includes:
sequentially taking various operation parameters of the comparison parameters as target operation parameters, calculating the similarity between the target operation parameters of every two adjacent preset time periods, and recording the similarity as comparison similarity;
calculating the variation amplitude of the contrast similarity of each target operation parameter;
and taking the target operation parameter corresponding to the maximum change amplitude as the effective operation parameter.
Under normal conditions, the similarity of the target operation parameters in adjacent preset time periods is higher, fluctuation of the similarity is smaller, when faults occur, severe fluctuation of the similarity is usually caused, the fluctuation of the similarity is reflected by the change amplitude, the target operation parameter with the most severe fluctuation of the similarity can reflect the fault condition most effectively, therefore, fault risk judgment is carried out according to the target operation parameter with the most severe fluctuation of the similarity, and accuracy of judgment results can be ensured.
Optionally, the step of determining one of the operating parameters as an effective operating parameter according to the comparison parameter includes:
sequentially taking various operation parameters of the comparison parameters as target operation parameters, and calculating the average value of the target operation parameters of each preset time period;
Calculating the fluctuation amplitude of the average value of each target operation parameter;
And taking the target operation parameter corresponding to the maximum fluctuation amplitude as the effective operation parameter.
The fluctuation amplitude of the average value of the target operation parameters in each preset time period reflects the fluctuation condition of the target operation parameters, when faults occur, the target operation parameters can often cause severe fluctuation, the fault risk is judged according to the target operation parameters with the most severe fluctuation, and the accuracy of the judging result can be ensured.
Preferably, step A3 comprises:
And if at least one of the effective similarities corresponding to the related household appliances exceeds a preset similarity threshold, judging that the related household appliances have fault risks.
Preferably, the related home appliances are: and the type of the household appliance is the same as that of the fault household appliance, and the deviation between the service time of the household appliance and the service time of the fault household appliance is within a preset range.
In a second aspect, the application provides a home appliance maintenance early warning device, which is applied to a server, wherein the server is in communication connection with a plurality of home appliances; the household electrical appliance maintenance early warning device comprises:
The first acquisition module is used for acquiring the operation parameters of a plurality of latest preset time periods before the fault report is sent by the fault household appliance when the fault report sent by the fault household appliance is received, marking the operation parameters as comparison parameters, and acquiring the operation parameters of the related household appliance in the adjacent area of the fault household appliance in the latest preset time period as target parameters;
the first calculation module is used for calculating the effective similarity between the target parameter and each comparison parameter of each related household appliance;
The first judging module is used for judging whether each related household appliance has a fault risk according to the effective similarity;
And the first early warning module is used for sending maintenance early warning information to the associated user side of the related household electrical appliance with fault risk.
When the household electrical appliance is in fault, the device takes the operation parameter of the fault household electrical appliance at a certain time before the fault as a reference and is used for comparing with the nearest actual operation parameter of the related household electrical appliance in the adjacent area of the fault household electrical appliance, so as to determine whether each related household electrical appliance has fault risk, and further, when the fault risk exists, the device timely sends maintenance early warning information so as to remind the corresponding household electrical appliance to be overhauled, thereby preventing the related household electrical appliance from having similar faults; because the natural environment conditions born by all the household appliances in the adjacent area are similar, similar faults are easier to occur, when the household appliances are in fault, the probability of similar faults of the related household appliances in the adjacent area is high, the related household appliances in the adjacent area are detected in the mode, the related household appliances can be effectively prevented from being in subsequent similar faults, and compared with the prior art, the training of a neural network model is not required to be performed by collecting a large amount of effective data, and the method is simpler and more practical.
In a third aspect, the present application provides an electronic device, comprising a processor and a memory, the memory storing a computer program executable by the processor, when executing the computer program, running steps in a home appliance maintenance pre-warning method as described above.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs steps in a home appliance maintenance pre-warning method as described hereinbefore.
The beneficial effects are that:
The home appliance maintenance early warning method and the related equipment provided by the application take the operation parameters of a certain time before the failure of the failed home appliance as references, and are used for comparing the latest actual operation parameters of the related home appliances in the vicinity of the failed home appliance, so as to determine whether each related home appliance has a failure risk, and further timely send maintenance early warning information when the failure risk exists, so as to remind the corresponding home appliance to be overhauled, thereby preventing the related home appliance from having similar failure; because the natural environment conditions born by all the household appliances in the adjacent area are similar, similar faults are easier to occur, when the household appliances are in fault, the probability of similar faults of the related household appliances in the adjacent area is high, the related household appliances in the adjacent area are detected in the mode, the related household appliances can be effectively prevented from being in subsequent similar faults, and compared with the prior art, the training of a neural network model is not required to be performed by collecting a large amount of effective data, and the method is simpler and more practical.
Drawings
Fig. 1 is a flowchart of a home appliance maintenance early warning method according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of a home appliance maintenance early warning device according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Description of the reference numerals: 1. a first acquisition module; 2. a first computing module; 3. a first judgment module; 4. a first early warning module; 301. A processor; 302. a memory; 303. a communication bus.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that: like reference numerals and letters designate like species in the following figures, and thus once a certain is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a schematic diagram of a home appliance maintenance early warning method according to some embodiments of the present application, which is applied to a server, and the server is communicatively connected to a plurality of home appliances; the household appliance maintenance early warning method comprises the following steps:
A1. When a fault report sent by a fault household appliance is received, acquiring operation parameters of the fault household appliance in a plurality of latest preset time periods before the fault report is sent, marking the operation parameters as comparison parameters, and acquiring operation parameters of related household appliances in a neighboring area of the fault household appliance in a latest preset time period, marking the operation parameters as target parameters;
A2. Calculating the effective similarity between the target parameters and the comparison parameters of each related household appliance;
A3. judging whether each related household appliance has fault risks according to the effective similarity;
A4. And sending maintenance early warning information to the associated user side of the related household appliance with fault risk.
When the household electrical appliance is in fault, the method takes the operation parameter of the fault household electrical appliance at a certain time before the fault as a reference, and is used for comparing the latest actual operation parameter of the related household electrical appliance in the area adjacent to the fault household electrical appliance, so as to determine whether each related household electrical appliance has fault risk, and further, when the fault risk exists, the method timely sends maintenance early warning information so as to remind the corresponding household electrical appliance to maintain, thereby preventing the related household electrical appliance from having similar faults; because the natural environment conditions born by all the household appliances in the adjacent area are similar, similar faults are easier to occur, when the household appliances are in fault, the probability of similar faults of the related household appliances in the adjacent area is high, the related household appliances in the adjacent area are detected in the mode, the related household appliances can be effectively prevented from being in subsequent similar faults, and compared with the prior art, the training of a neural network model is not required to be performed by collecting a large amount of effective data, and the method is simpler and more practical.
The associated user terminal of the home device is a terminal of a user bound to the home device, where the user may be an owner of the home device, or may be an after-sales service person of the home device, and the terminal may be any user device capable of implementing interaction with a server through any form of wired and/or wireless connection (e.g., wi-Fi, LAN, cellular, coaxial cable, etc.), including but not limited to: smart phones, non-smart phones, tablet computers, laptop personal computers, desktop personal computers, minicomputers, midrange computers, mainframe computers, and the like.
The server can be an independent device or a cluster server, and can provide data support for a plurality of terminals at the same time. The servers may include, but are not limited to, physical servers and/or virtual servers.
The home appliance judges whether the home appliance fails or not by detecting the running parameters of the home appliance in real time, and sends a failure report to the server when judging the home appliance fails. Further, the home appliance can judge the fault type according to the operation parameters of the home appliance, and the fault type is contained in a fault report to be sent. In addition, the operation parameters collected in real time are sent to the server in the working process of the household electrical appliance, and the server records according to actual needs (for example, the server can record all operation parameters received in history or record operation parameters of a plurality of latest preset time periods, the operation parameters of one preset time period are used as a group of operation parameters, and in the latter case, if the number of the groups of the operation parameters of a certain household electrical appliance currently recorded reaches the threshold value of the preset number of the sections, each time a group of operation parameters is received, the group of operation parameters received first in the record is deleted so as to avoid occupying excessive storage space).
In step A1, acquiring operation parameters of a most recent preset number of segments threshold for a preset time period before a fault report is sent to a fault home appliance; the specific value of the preset segment number threshold value can be set according to actual needs. Thus, a threshold number of preset segments of comparison parameters are obtained, each comparison parameter and each target parameter comprise a set of operation parameters, the type of specific parameters included in each set of operation parameters can be set according to actual needs, for example, the type of operation parameters can include at least one of an operation current, an operation voltage, operation vibration data, an operation noise, an operation temperature and an operation pressure, that is, each set of operation parameters comprises at least one of an operation current of a preset time period, an operation voltage of a preset time period, operation vibration data of a preset time period, an operation noise of a preset time period, an operation temperature of a preset time period and an operation pressure of a preset time period.
The length of the preset time period can be set according to actual needs.
Specifically, when the operation parameter includes one of an operation current, an operation voltage, operation vibration data, operation noise, an operation temperature, and an operation pressure;
The step A2 comprises the following steps:
And calculating the similarity of one operation parameter included in the target parameters of each related household appliance and one operation parameter included in each comparison parameter as the effective similarity of the target parameters of each related household appliance and each comparison parameter.
For example, if the parameter type included in the operation parameter is an operation current, the similarity between the operation current in the target parameter of the related home appliance and the operation current in each comparison parameter is calculated, and the similarity is used as an effective similarity for performing subsequent fault risk judgment. When the specific parameter type of the operation parameter is other parameters, the processing procedure is the same as that.
The similarity can be calculated by selecting a similarity calculation method in the prior art according to actual needs.
Specifically, when the operation parameters include a plurality of kinds of operation current, operation voltage, operation vibration data, operation noise, operation temperature, operation pressure;
The step A2 comprises the following steps:
Determining an operation parameter as an effective operation parameter according to the comparison parameter;
And calculating the similarity of the effective operation parameters in the target parameters of each related household appliance and the effective operation parameters in the comparison parameters, and taking the similarity as the effective similarity of the target parameters of each related household appliance and the comparison parameters.
One of the operating parameters is selected as an effective operating parameter, and effective similarity is calculated only for the effective operating parameter, so that the calculation processing amount is reduced, and the processing efficiency is improved.
In some embodiments, the step of determining an operating parameter as an effective operating parameter based on the comparison parameter comprises:
sequentially taking various operation parameters of the comparison parameters as target operation parameters, calculating the similarity between every two adjacent target operation parameters in a preset time period, and marking the similarity as the comparison similarity;
calculating the variation amplitude of the contrast similarity of each target operation parameter;
And taking the target operation parameter corresponding to the maximum variation amplitude as the effective operation parameter.
Under normal conditions, the similarity of the target operation parameters in adjacent preset time periods is higher, fluctuation of the similarity is smaller, when faults occur, severe fluctuation of the similarity is usually caused, the fluctuation of the similarity is reflected by the change amplitude, the target operation parameter with the most severe fluctuation of the similarity can reflect the fault condition most effectively, therefore, fault risk judgment is carried out according to the target operation parameter with the most severe fluctuation of the similarity, and accuracy of judgment results can be ensured.
For example, the types of the operation parameters include two kinds of operation current and operation voltage, and the operation current and the operation voltage of the comparison parameters are sequentially used as target operation parameters; when working current is taken as a target operation parameter, calculating the similarity of the working current in two adjacent preset time periods (for example, according to time sequence, the working current in a plurality of preset time periods is respectively marked as I1, I2, I3 and I4, then the similarity of I1 and I2, the similarity of I2 and I3 and the similarity of I3 and I4 are calculated and marked as comparison similarity, so that a plurality of comparison similarity are obtained, and then the difference between the maximum value and the minimum value of the comparison similarity is calculated to obtain the variation amplitude of the comparison similarity of the working current; when the working voltage is taken as a target operation parameter, a similar method is adopted to obtain the variation amplitude of the contrast similarity of the working voltage; if the variation amplitude of the contrast similarity of the working current is larger than that of the contrast similarity of the working voltage, the working current is used as an effective operation parameter, otherwise, the working voltage is used as the effective operation parameter.
In other embodiments, the step of determining an operating parameter as an effective operating parameter based on the comparison parameter comprises:
sequentially taking various operation parameters of the comparison parameters as target operation parameters, and calculating the average value of the target operation parameters of each preset time period;
calculating the fluctuation amplitude of the average value of each target operation parameter;
and taking the target operation parameter corresponding to the maximum fluctuation amplitude as the effective operation parameter.
The fluctuation amplitude of the average value of the target operation parameters in each preset time period reflects the fluctuation condition of the target operation parameters, when faults occur, the target operation parameters can often cause severe fluctuation, the fault risk is judged according to the target operation parameters with the most severe fluctuation, and the accuracy of the judging result can be ensured.
For example, the types of the operation parameters include two kinds of operation current and operation voltage, and the operation current and the operation voltage of the comparison parameters are sequentially used as target operation parameters; when the working current is taken as a target operation parameter, calculating an average value of the target operation parameter of each preset time period (for example, according to time sequence, the working currents of a plurality of preset time periods are respectively recorded as I1, I2, I3 and I4, then calculating an average value of current data in I1, an average value of current data in I2, an average value of current data in I3 and an average value of current data in I4), thereby obtaining an average value of a plurality of working currents, and then calculating a difference value between a maximum value and a minimum value of the average values to obtain a fluctuation amplitude of the average value of the working current; when the working voltage is taken as a target operation parameter, a similar method is adopted to obtain the fluctuation amplitude of the average value of the working voltage; if the fluctuation amplitude of the average value of the working current is larger than that of the average value of the working voltage, the working current is used as an effective operation parameter, otherwise, the working voltage is used as the effective operation parameter.
In this embodiment, step A3 includes:
if at least one of the corresponding effective similarities of the related household appliances exceeds a preset similarity threshold (which can be set according to actual needs), judging that the related household appliances have fault risks.
In practical application, when the operation parameters include a plurality of operation current, operation voltage, operation vibration data, operation noise, operation temperature and operation pressure, corresponding effective similarity can be obtained for each operation parameter (that is, various operation parameters are sequentially used as effective operation parameters, and corresponding effective similarity is obtained, and the obtaining process of the effective similarity refers to the foregoing), in step A3, for a related household appliance, if at least one of the effective similarities corresponding to each operation parameter exceeds a preset similarity threshold, the malformation value of the operation parameter of the corresponding type is set to 1, otherwise, the malformation value of the operation parameter of the corresponding type is set to 0, and then a weighted sum of the malformation values of the operation parameters is calculated (the weighted sum of the operation parameters can be set according to practical needs), and if the weighted sum exceeds the preset malformation threshold (can be set according to practical needs), the related household appliance is determined to have a fault risk.
Wherein the neighboring area is an area of a preset size and shape centered on the failed home device. For example, but not limited to, a circular area of a preset radius (the radius size may be set according to actual needs). The location information of each home appliance may be recorded in the server in advance, or may be uploaded to the server in real time. In step A1, a neighboring area is determined according to the location information of the faulty home appliance, the related home appliances located in the neighboring area are determined according to the location information of the other home appliances, and finally the operation parameters of each related home appliance in the latest preset time period are extracted and recorded as target parameters.
Preferably, the relevant home appliances are: the type is the same as the type of the fault household appliance, and the deviation between the service time of the fault household appliance and the service time of the fault household appliance is in a preset range (which can be set according to actual needs).
Because the household appliances with the same model and similar service time are more likely to have the same faults, the identification objects are limited to the household appliances with the same model and similar service time as the faulty household appliances, so that the similar faults of the related household appliances can be effectively prevented, the data processing capacity can be reduced, and the detection efficiency can be improved.
Further, if there is a fault risk of the excessive related home appliances, the probability of the same type of home appliances having the same fault is greatly improved, and at this time, the fault risk of other home appliances of the same type (that is, the deviation between the service time of the same type but the service time of the faulty home appliances is not in the preset range) can be further checked to further effectively prevent the similar faults of other home appliances; thus, in some preferred embodiments, after step A4, the steps are further comprised of:
A5. If the number of the related household appliances with the fault risk exceeds a preset number threshold (which can be set according to actual needs), acquiring the operation parameters of the next related household appliances in the adjacent area of the fault household appliances in the latest preset time period, and recording the operation parameters as secondary target parameters; the secondary related household electrical appliance is a household electrical appliance with the same model as the fault household electrical appliance, but the deviation between the service time of the fault household electrical appliance and the service time of the fault household electrical appliance is not in a preset range;
A6. calculating the effective similarity between the secondary target parameters and the comparison parameters of each secondary related household appliance (the specific process is similar to the step A2);
A7. Judging whether each related household appliance has fault risks according to the effective similarity obtained in the step A6 (the specific process is similar to the step A3);
A8. and sending maintenance early warning information to the associated user side of the secondary related household appliance with fault risk.
According to the home appliance maintenance early warning method, when a fault report sent by a fault home appliance is received, operation parameters of the fault home appliance in a plurality of latest preset time periods before the fault report is sent are obtained and are recorded as comparison parameters, operation parameters of related home appliances in a nearby area of the fault home appliance in a latest preset time period are obtained and are recorded as target parameters, effective similarity between the target parameters of each related home appliance and each comparison parameter is calculated, whether each related home appliance has fault risks or not is judged according to the effective similarity, and maintenance early warning information is sent to a related user side of the related home appliance with the fault risks; therefore, similar faults of related household appliances can be effectively prevented, compared with the prior art, the neural network model training method is simpler and more practical, and a large amount of effective data are not required to be collected for training the neural network model.
Referring to fig. 2, the application provides a home appliance maintenance early warning device, which is applied to a server, wherein the server is in communication connection with a plurality of home appliances; the household electrical appliances maintenance early warning device includes:
the first obtaining module 1 is configured to obtain, when a fault report sent by a faulty home appliance is received, operation parameters of the faulty home appliance in a plurality of latest preset time periods before the fault report is sent, and record the operation parameters as comparison parameters, and obtain operation parameters of relevant home appliances in a neighboring area of the faulty home appliance in a latest preset time period, and record the operation parameters as target parameters;
the first calculating module 2 is used for calculating the effective similarity between the target parameter and each contrast parameter of each related household appliance;
The first judging module 3 is used for judging whether each related household appliance has a fault risk according to the effective similarity;
the first early warning module 4 is configured to send maintenance early warning information to an associated user side of the related home appliance having the fault risk.
When the household electrical appliance is in fault, the device takes the operation parameter of the fault household electrical appliance at a certain time before the fault as a reference and is used for comparing with the nearest actual operation parameter of the related household electrical appliance in the adjacent area of the fault household electrical appliance, so as to determine whether each related household electrical appliance has fault risk, and further, when the fault risk exists, the device timely sends maintenance early warning information so as to remind the corresponding household electrical appliance to be overhauled, thereby preventing the related household electrical appliance from having similar faults; because the natural environment conditions born by all the household appliances in the adjacent area are similar, similar faults are easier to occur, when the household appliances are in fault, the probability of similar faults of the related household appliances in the adjacent area is high, the related household appliances in the adjacent area are detected in the mode, the related household appliances can be effectively prevented from being in subsequent similar faults, and compared with the prior art, the training of a neural network model is not required to be performed by collecting a large amount of effective data, and the method is simpler and more practical.
The associated user terminal of the home device is a terminal of a user bound to the home device, where the user may be an owner of the home device, or may be an after-sales service person of the home device, and the terminal may be any user device capable of implementing interaction with a server through any form of wired and/or wireless connection (e.g., wi-Fi, LAN, cellular, coaxial cable, etc.), including but not limited to: smart phones, non-smart phones, tablet computers, laptop personal computers, desktop personal computers, minicomputers, midrange computers, mainframe computers, and the like.
The server can be an independent device or a cluster server, and can provide data support for a plurality of terminals at the same time. The servers may include, but are not limited to, physical servers and/or virtual servers.
The home appliance judges whether the home appliance fails or not by detecting the running parameters of the home appliance in real time, and sends a failure report to the server when judging the home appliance fails. Further, the home appliance can judge the fault type according to the operation parameters of the home appliance, and the fault type is contained in a fault report to be sent. In addition, the operation parameters collected in real time are sent to the server in the working process of the household electrical appliance, and the server records according to actual needs (for example, the server can record all operation parameters received in history or record operation parameters of a plurality of latest preset time periods, the operation parameters of one preset time period are used as a group of operation parameters, and in the latter case, if the number of the groups of the operation parameters of a certain household electrical appliance currently recorded reaches the threshold value of the preset number of the sections, each time a group of operation parameters is received, the group of operation parameters received first in the record is deleted so as to avoid occupying excessive storage space).
The first obtaining module 1 obtains the operation parameters of the most recent preset time periods before the fault report is sent by the fault household appliance when obtaining the operation parameters of the most recent preset time periods before the fault report is sent by the fault household appliance; the specific value of the preset segment number threshold value can be set according to actual needs. Thus, a threshold number of preset segments of comparison parameters are obtained, each comparison parameter and each target parameter comprise a set of operation parameters, the type of specific parameters included in each set of operation parameters can be set according to actual needs, for example, the type of operation parameters can include at least one of an operation current, an operation voltage, operation vibration data, an operation noise, an operation temperature and an operation pressure, that is, each set of operation parameters comprises at least one of an operation current of a preset time period, an operation voltage of a preset time period, operation vibration data of a preset time period, an operation noise of a preset time period, an operation temperature of a preset time period and an operation pressure of a preset time period.
The length of the preset time period can be set according to actual needs.
Specifically, when the operation parameter includes one of an operation current, an operation voltage, operation vibration data, operation noise, an operation temperature, and an operation pressure;
The first calculation module 2 performs, when calculating the effective similarity between the target parameter and each contrast parameter of each relevant home appliance:
And calculating the similarity of one operation parameter included in the target parameters of each related household appliance and one operation parameter included in each comparison parameter as the effective similarity of the target parameters of each related household appliance and each comparison parameter.
For example, if the parameter type included in the operation parameter is an operation current, the similarity between the operation current in the target parameter of the related home appliance and the operation current in each comparison parameter is calculated, and the similarity is used as an effective similarity for performing subsequent fault risk judgment. When the specific parameter type of the operation parameter is other parameters, the processing procedure is the same as that.
The similarity can be calculated by selecting a similarity calculation method in the prior art according to actual needs.
Specifically, when the operation parameters include a plurality of kinds of operation current, operation voltage, operation vibration data, operation noise, operation temperature, operation pressure;
The first calculation module 2 performs, when calculating the effective similarity between the target parameter and each contrast parameter of each relevant home appliance:
Determining an operation parameter as an effective operation parameter according to the comparison parameter;
And calculating the similarity of the effective operation parameters in the target parameters of each related household appliance and the effective operation parameters in the comparison parameters, and taking the similarity as the effective similarity of the target parameters of each related household appliance and the comparison parameters.
One of the operating parameters is selected as an effective operating parameter, and effective similarity is calculated only for the effective operating parameter, so that the calculation processing amount is reduced, and the processing efficiency is improved.
In some embodiments, the first computing module 2 performs, when determining an operating parameter as the effective operating parameter based on the comparison parameter:
sequentially taking various operation parameters of the comparison parameters as target operation parameters, calculating the similarity between every two adjacent target operation parameters in a preset time period, and marking the similarity as the comparison similarity;
calculating the variation amplitude of the contrast similarity of each target operation parameter;
And taking the target operation parameter corresponding to the maximum variation amplitude as the effective operation parameter.
Under normal conditions, the similarity of the target operation parameters in adjacent preset time periods is higher, fluctuation of the similarity is smaller, when faults occur, severe fluctuation of the similarity is usually caused, the fluctuation of the similarity is reflected by the change amplitude, the target operation parameter with the most severe fluctuation of the similarity can reflect the fault condition most effectively, therefore, fault risk judgment is carried out according to the target operation parameter with the most severe fluctuation of the similarity, and accuracy of judgment results can be ensured.
For example, the types of the operation parameters include two kinds of operation current and operation voltage, and the operation current and the operation voltage of the comparison parameters are sequentially used as target operation parameters; when working current is taken as a target operation parameter, calculating the similarity of the working current in two adjacent preset time periods (for example, according to time sequence, the working current in a plurality of preset time periods is respectively marked as I1, I2, I3 and I4, then the similarity of I1 and I2, the similarity of I2 and I3 and the similarity of I3 and I4 are calculated and marked as comparison similarity, so that a plurality of comparison similarity are obtained, and then the difference between the maximum value and the minimum value of the comparison similarity is calculated to obtain the variation amplitude of the comparison similarity of the working current; when the working voltage is taken as a target operation parameter, a similar method is adopted to obtain the variation amplitude of the contrast similarity of the working voltage; if the variation amplitude of the contrast similarity of the working current is larger than that of the contrast similarity of the working voltage, the working current is used as an effective operation parameter, otherwise, the working voltage is used as the effective operation parameter.
In other embodiments, the first computing module 2 performs, when determining an operating parameter as the effective operating parameter based on the comparison parameter:
sequentially taking various operation parameters of the comparison parameters as target operation parameters, and calculating the average value of the target operation parameters of each preset time period;
calculating the fluctuation amplitude of the average value of each target operation parameter;
and taking the target operation parameter corresponding to the maximum fluctuation amplitude as the effective operation parameter.
The fluctuation amplitude of the average value of the target operation parameters in each preset time period reflects the fluctuation condition of the target operation parameters, when faults occur, the target operation parameters can often cause severe fluctuation, the fault risk is judged according to the target operation parameters with the most severe fluctuation, and the accuracy of the judging result can be ensured.
For example, the types of the operation parameters include two kinds of operation current and operation voltage, and the operation current and the operation voltage of the comparison parameters are sequentially used as target operation parameters; when the working current is taken as a target operation parameter, calculating an average value of the target operation parameter of each preset time period (for example, according to time sequence, the working currents of a plurality of preset time periods are respectively recorded as I1, I2, I3 and I4, then calculating an average value of current data in I1, an average value of current data in I2, an average value of current data in I3 and an average value of current data in I4), thereby obtaining an average value of a plurality of working currents, and then calculating a difference value between a maximum value and a minimum value of the average values to obtain a fluctuation amplitude of the average value of the working current; when the working voltage is taken as a target operation parameter, a similar method is adopted to obtain the fluctuation amplitude of the average value of the working voltage; if the fluctuation amplitude of the average value of the working current is larger than that of the average value of the working voltage, the working current is used as an effective operation parameter, otherwise, the working voltage is used as the effective operation parameter.
In this embodiment, when determining whether each related home appliance has a fault risk according to the effective similarity, the first determining module 3 performs:
if at least one of the corresponding effective similarities of the related household appliances exceeds a preset similarity threshold (which can be set according to actual needs), judging that the related household appliances have fault risks.
In practical applications, when the operation parameters include a plurality of operation current, operation voltage, operation vibration data, operation noise, operation temperature, and operation pressure, the corresponding effective similarity may also be obtained for each operation parameter (that is, each operation parameter is sequentially taken as an effective operation parameter, and the corresponding effective similarity is obtained, and the obtaining process of the effective similarity refers to the foregoing), so that when judging whether each related home appliance has a fault risk according to the effective similarity, the first judging module 3 executes: for a related household appliance, if at least one of the effective similarities corresponding to each operating parameter exceeds a preset similarity threshold, setting the malformation value of the operating parameter of the corresponding type to be 1, otherwise setting the malformation value of the operating parameter of the corresponding type to be 0, then calculating a weighted sum of the malformation values of the operating parameters (the weight of each operating parameter can be set according to actual needs), and if the weighted sum exceeds the preset malformation threshold (can be set according to actual needs), judging that the related household appliance has fault risk.
Wherein the neighboring area is an area of a preset size and shape centered on the failed home device. For example, but not limited to, a circular area of a preset radius (the radius size may be set according to actual needs). The location information of each home appliance may be recorded in the server in advance, or may be uploaded to the server in real time. Thus, the first obtaining module 1 performs, when obtaining the operation parameter of the related home appliance in the vicinity of the failed home appliance in the latest one preset time period, as the target parameter: and determining a neighboring area according to the position information of the fault household appliance, determining related household appliances positioned in the neighboring area according to the position information of other household appliances, and finally extracting the operation parameters of each related household appliance in the latest preset time period and recording the operation parameters as target parameters.
Preferably, the relevant home appliances are: the type is the same as the type of the fault household appliance, and the deviation between the service time of the fault household appliance and the service time of the fault household appliance is in a preset range (which can be set according to actual needs).
Because the household appliances with the same model and similar service time are more likely to have the same faults, the identification objects are limited to the household appliances with the same model and similar service time as the faulty household appliances, so that the similar faults of the related household appliances can be effectively prevented, the data processing capacity can be reduced, and the detection efficiency can be improved.
Further, if there is a fault risk of the excessive related home appliances, the probability of the same type of home appliances having the same fault is greatly improved, and at this time, the fault risk of other home appliances of the same type (that is, the deviation between the service time of the same type but the service time of the faulty home appliances is not in the preset range) can be further checked to further effectively prevent the similar faults of other home appliances; thus, in some preferred embodiments, the home appliance maintenance pre-warning device further comprises:
The second acquisition module is used for acquiring the operation parameters of the secondary related household appliances in the adjacent area of the fault household appliances in the latest preset time period when the number of the related household appliances with fault risks exceeds a preset number threshold (which can be set according to actual needs), and recording the operation parameters as secondary target parameters; the secondary related household electrical appliance is a household electrical appliance with the same model as the fault household electrical appliance, but the deviation between the service time of the fault household electrical appliance and the service time of the fault household electrical appliance is not in a preset range;
the second calculation module is used for calculating the effective similarity between the secondary target parameter and each contrast parameter of each secondary related household appliance;
The second judging module is used for judging whether each related household appliance has a fault risk according to the effective similarity obtained by the second calculating module;
And the second early warning module is used for sending maintenance early warning information to the associated user side of the secondary related household electrical appliance with fault risk.
As can be seen from the above, the home appliance maintenance early warning device obtains the operation parameters of a plurality of latest preset time periods before the fault home appliance sends the fault report by receiving the fault report sent by the fault home appliance, marks the operation parameters as comparison parameters, obtains the operation parameters of related home appliances in the adjacent area of the fault home appliance in the latest preset time period, marks the operation parameters as target parameters, calculates the effective similarity between the target parameters and the comparison parameters of each related home appliance, judges whether the related home appliance has fault risk according to the effective similarity, and sends maintenance early warning information to the related user side of the related home appliance with the fault risk; therefore, similar faults of related household appliances can be effectively prevented, compared with the prior art, the neural network model training method is simpler and more practical, and a large amount of effective data are not required to be collected for training the neural network model.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application, where the electronic device includes: processor 301 and memory 302, the processor 301 and the memory 302 being interconnected and in communication with each other by a communication bus 303 and/or other form of connection mechanism (not shown), the memory 302 storing a computer program executable by the processor 301, the processor 301 executing the computer program when the electronic device is running to perform the home appliance repair pre-warning method in any of the alternative implementations of the above embodiments to perform the following functions: when a fault report sent by a fault household appliance is received, acquiring operation parameters of the fault household appliance in a plurality of latest preset time periods before the fault report is sent, marking the operation parameters as comparison parameters, acquiring operation parameters of related household appliances in a nearby area of the fault household appliance in a latest preset time period, marking the operation parameters as target parameters, calculating the effective similarity between the target parameters of each related household appliance and each comparison parameter, judging whether each related household appliance has a fault risk according to the effective similarity, and sending maintenance early warning information to a related user side of the related household appliance with the fault risk.
The embodiment of the application provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, performs the home appliance maintenance early warning method in any optional implementation manner of the above embodiment, so as to implement the following functions: when a fault report sent by a fault household appliance is received, acquiring operation parameters of the fault household appliance in a plurality of latest preset time periods before the fault report is sent, marking the operation parameters as comparison parameters, acquiring operation parameters of related household appliances in a nearby area of the fault household appliance in a latest preset time period, marking the operation parameters as target parameters, calculating the effective similarity between the target parameters of each related household appliance and each comparison parameter, judging whether each related household appliance has a fault risk according to the effective similarity, and sending maintenance early warning information to a related user side of the related household appliance with the fault risk. The computer readable storage medium may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable Programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, EEPROM for short), erasable Programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM for short), programmable Read-Only Memory (PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
Further, the units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Furthermore, functional modules in various embodiments of the present application may be integrated together to form a single portion, or each module may exist alone, or two or more modules may be integrated to form a single portion.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (5)
1. The home appliance maintenance early warning method is applied to a server, and the server is in communication connection with a plurality of home appliances; the household appliance maintenance early warning method is characterized by comprising the following steps of:
A1. When a fault report sent by a fault household appliance is received, acquiring operation parameters of the fault household appliance in a plurality of latest preset time periods before the fault report is sent, marking the operation parameters as comparison parameters, and acquiring operation parameters of related household appliances in a neighboring area of the fault household appliance in the latest preset time periods, marking the operation parameters as target parameters; the adjacent area is an area of preset size and shape centered on the failed home device; the related household electrical appliances are as follows: the type of the household appliance is the same as that of the fault household appliance, and the deviation between the service time of the household appliance and the service time of the fault household appliance is within a preset range;
A2. calculating the effective similarity between the target parameter and each comparison parameter of each related household appliance;
A3. Judging whether each related household appliance has a fault risk according to the effective similarity;
A4. sending maintenance early warning information to an associated user side of the related household electrical appliance with fault risk;
The operation parameters comprise a plurality of operation current, operation voltage, operation vibration data, operation noise, operation temperature and operation pressure;
The step A2 comprises the following steps:
Determining one of the operating parameters as an effective operating parameter according to the comparison parameter;
Calculating the similarity of the effective operation parameter in the target parameters of each related household appliance and the effective operation parameter in the comparison parameters, and taking the similarity as the effective similarity of the target parameters of each related household appliance and the comparison parameters;
The step of determining one of the operating parameters as an effective operating parameter based on the comparison parameter comprises:
sequentially taking various operation parameters of the comparison parameters as target operation parameters, calculating the similarity between the target operation parameters of every two adjacent preset time periods, and recording the similarity as comparison similarity;
calculating the variation amplitude of the contrast similarity of each target operation parameter;
taking the target operation parameter corresponding to the maximum variation amplitude as the effective operation parameter;
or the step of determining one of the operating parameters as an effective operating parameter based on the comparison parameter comprises:
The step of determining one of the operating parameters as an effective operating parameter based on the comparison parameter comprises:
sequentially taking various operation parameters of the comparison parameters as target operation parameters, and calculating the average value of the target operation parameters of each preset time period;
Calculating the fluctuation amplitude of the average value of each target operation parameter;
And taking the target operation parameter corresponding to the maximum fluctuation amplitude as the effective operation parameter.
2. The home appliance maintenance pre-warning method according to claim 1, wherein the step A3 includes:
And if at least one of the effective similarities corresponding to the related household appliances exceeds a preset similarity threshold, judging that the related household appliances have fault risks.
3. The household appliance maintenance early warning device is applied to a server, and the server is in communication connection with a plurality of household appliances; the household appliance maintenance early warning device is characterized by comprising:
the first acquisition module is used for acquiring the operation parameters of a plurality of latest preset time periods before the fault report is sent by the fault household appliance when the fault report sent by the fault household appliance is received, marking the operation parameters as comparison parameters, and acquiring the operation parameters of the related household appliance in the adjacent area of the fault household appliance in the latest preset time period as target parameters; the adjacent area is an area of preset size and shape centered on the failed home device; the related household electrical appliances are as follows: the type of the household appliance is the same as that of the fault household appliance, and the deviation between the service time of the household appliance and the service time of the fault household appliance is within a preset range;
the first calculation module is used for calculating the effective similarity between the target parameter and each comparison parameter of each related household appliance;
The first judging module is used for judging whether each related household appliance has a fault risk according to the effective similarity;
the first early warning module is used for sending maintenance early warning information to the associated user side of the related household electrical appliance with fault risk;
The operation parameters comprise a plurality of operation current, operation voltage, operation vibration data, operation noise, operation temperature and operation pressure;
The first calculating module performs, when calculating the effective similarity between the target parameter and each of the comparison parameters of each of the related home devices:
Determining one of the operating parameters as an effective operating parameter according to the comparison parameter;
Calculating the similarity of the effective operation parameter in the target parameters of each related household appliance and the effective operation parameter in the comparison parameters, and taking the similarity as the effective similarity of the target parameters of each related household appliance and the comparison parameters;
the first calculation module performs, when determining one of the operating parameters as an effective operating parameter based on the comparison parameter:
sequentially taking various operation parameters of the comparison parameters as target operation parameters, calculating the similarity between the target operation parameters of every two adjacent preset time periods, and recording the similarity as comparison similarity;
calculating the variation amplitude of the contrast similarity of each target operation parameter;
taking the target operation parameter corresponding to the maximum variation amplitude as the effective operation parameter;
or the first calculation module performs, when determining one of the operation parameters as an effective operation parameter according to the comparison parameter:
The step of determining one of the operating parameters as an effective operating parameter based on the comparison parameter comprises:
sequentially taking various operation parameters of the comparison parameters as target operation parameters, and calculating the average value of the target operation parameters of each preset time period;
Calculating the fluctuation amplitude of the average value of each target operation parameter;
And taking the target operation parameter corresponding to the maximum fluctuation amplitude as the effective operation parameter.
4. An electronic device comprising a processor and a memory, the memory storing a computer program executable by the processor, when executing the computer program, running the steps of the home appliance maintenance pre-warning method of any one of claims 1-2.
5. A computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the home appliance maintenance pre-warning method according to any one of claims 1-2.
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